1 package org.neuroph.samples;
   2 
   3 import org.neuroph.core.NeuralNetwork;
   4 import org.neuroph.nnet.Perceptron;
   5 import org.neuroph.core.learning.DataSet;
   6 import org.neuroph.core.learning.TrainingElement;
   7 import org.neuroph.core.learning.SupervisedTrainingElement;
   8 import java.util.Vector;
   9 
  10 /**
  11  * This sample shows how to create, train, save and load simple Perceptron neural network 
  12  */
  13 public class PerceptronSample {
  14 
  15   public static void main(String args[]) {
  16 
  17     // create training set (logical AND function)
  18     DataSet trainingSet = new DataSet(2, 1);
  19     trainingSet.addRow(new DataSetRow(new double[] {
  20       0, 0
  21     }
  22     , new double[] {
  23       0
  24     }
  25     ));
  26     trainingSet.addRow(new DataSetRow(new double[] {
  27       0, 1
  28     }
  29     , new double[] {
  30       0
  31     }
  32     ));
  33     trainingSet.addRow(new DataSetRow(new double[] {
  34       1, 0
  35     }
  36     , new double[] {
  37       0
  38     }
  39     ));
  40     trainingSet.addRow(new DataSetRow(new double[] {
  41       1, 1
  42     }
  43     , new double[] {
  44       1
  45     }
  46     ));
  47 
  48     // create perceptron neural network
  49     NeuralNetwork myPerceptron = new Perceptron(2, 1);
  50 
  51     // learn the training set
  52     myPerceptron.learn(trainingSet);
  53 
  54     // test perceptron
  55     System.out.println("Testing trained perceptron");
  56     testNeuralNetwork(myPerceptron, trainingSet);
  57 
  58     // save trained perceptron
  59     myPerceptron.save("mySamplePerceptron.nnet");
  60 
  61     // load saved neural network
  62     NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");
  63 
  64     // test loaded neural network
  65     System.out.println("Testing loaded perceptron");
  66     testNeuralNetwork(loadedPerceptron, trainingSet);
  67   }
  68 
  69   public static void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {
  70 
  71     for (DataSetRow dataRow : tset.getRows()) {
  72 
  73       nnet.setInput(dataRow.getInput());
  74       nnet.calculate();
  75       double[ ] networkOutput = nnet.getOutput();
  76       System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );
  77       System.out.println(" Output: " + Arrays.toString(networkOutput) );
  78     }
  79   }
  80 }

   1 import org.neuroph.util.plugins.*;
   2 import org.neuroph.core.transfer.*;
   3 import org.neuroph.util.*;
   4 import org.neuroph.nnet.comp.layer.*;
   5 import org.neuroph.nnet.*;
   6 import org.neuroph.core.events.*;
   7 import org.neuroph.core.learning.error.*;
   8 import org.neuroph.core.data.sample.*;
   9 import org.neuroph.util.io.*;
  10 import org.neuroph.core.exceptions.*;
  11 import org.neuroph.core.learning.*;
  12 import org.neuroph.core.input.*;
  13 import org.neuroph.nnet.learning.*;
  14 import org.neuroph.util.benchmark.*;
  15 import org.neuroph.util.random.*;
  16 import org.neuroph.nnet.comp.*;
  17 import org.neuroph.core.learning.stop.*;
  18 import org.neuroph.core.data.*;
  19 import org.neuroph.core.*;
  20 import org.neuroph.nnet.comp.neuron.*;
  21 import org.neuroph.core.data.norm.*;
  22 
  23 import java.util.*;
  24 
  25 /**
  26  * This sample shows how to create, train, save and load simple Perceptron neural network 
  27  */
  28 void setup() {
  29 
  30   // create training set (logical AND function)
  31   DataSet trainingSet = new DataSet(2, 1);
  32   trainingSet.addRow(new DataSetRow(new double[] {
  33     0, 0
  34   }
  35   , new double[] {
  36     0
  37   }
  38   ));
  39   trainingSet.addRow(new DataSetRow(new double[] {
  40     0, 1
  41   }
  42   , new double[] {
  43     0
  44   }
  45   ));
  46   trainingSet.addRow(new DataSetRow(new double[] {
  47     1, 0
  48   }
  49   , new double[] {
  50     0
  51   }
  52   ));
  53   trainingSet.addRow(new DataSetRow(new double[] {
  54     1, 1
  55   }
  56   , new double[] {
  57     1
  58   }
  59   ));
  60 
  61   // create perceptron neural network
  62   NeuralNetwork myPerceptron = new Perceptron(2, 1);
  63 
  64   // learn the training set
  65   myPerceptron.learn(trainingSet);
  66 
  67   // test perceptron
  68   System.out.println("Testing trained perceptron");
  69   testNeuralNetwork(myPerceptron, trainingSet);
  70 
  71   // save trained perceptron
  72   myPerceptron.save("mySamplePerceptron.nnet");
  73 
  74   // load saved neural network
  75   NeuralNetwork loadedPerceptron = NeuralNetwork.load("mySamplePerceptron.nnet");
  76 
  77   // test loaded neural network
  78   System.out.println("Testing loaded perceptron");
  79   testNeuralNetwork(loadedPerceptron, trainingSet);
  80 }
  81 
  82 void testNeuralNetwork(NeuralNetwork nnet, DataSet tset) {
  83 
  84   for (DataSetRow dataRow : tset.getRows()) {
  85 
  86     nnet.setInput(dataRow.getInput());
  87     nnet.calculate();
  88     double[ ] networkOutput = nnet.getOutput();
  89     System.out.print("Input: " + Arrays.toString(dataRow.getInput()) );
  90     System.out.println(" Output: " + Arrays.toString(networkOutput) );
  91   }
  92 }